Improved Temporal Pooling for Perceptual Video Quality Assessment Using VMAF

The Video Multimethod Assessment Fusion (VMAF) method, proposed by Netflix, offers an automated estimation of perceptual video quality for each frame of a video sequence. Then, the arithmetic mean of the per-frame quality measurements is taken by default, in order to obtain an estimate of the overall Quality of Experience (QoE) of the video sequence. In this paper, we validate the hypothesis that the arithmetic mean conceals the bad quality frames, leading to an overestimation of the provided quality. We also show that the Minkowski mean (appropriately parametrized) approximates well the subjectively measured QoE, providing superior Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation Coefficient (PCC), and Root-Mean-Square-Error (RMSE) scores.